I’ve been writing on big data/analytics for the past 2 months, and I get a sense that people got a little data overloaded. So let me come back to the topic of influence and pick up where I left off.

In my previous writing on digital influence, we had a rather scientific and statistical discussion about validating algorithms which predict people’s influence. When you dig deeper into what influence vendors actually do to validate their algorithms, you quickly find that most influence scores cannot be trusted. Mainly because vendors don’t validate, overgeneralize, or validate their algorithm using flawed circular logic.

Another serious problem with most influence scoring models is “IEO.” You see the title; I really meant influence engine optimization (IEO) as opposed to search engine optimization (SEO). What is IEO? That will be the topic of discussion today and I promise it will be much less technical than my last post.

However, if you really want to understand the current state of influence scoring industry, you should still check out the following articles:

Google developed an innovative relevance ranking algorithm (a.k.a. PageRank) based on the hyperlink structure of the www. The PageRank algorithm basically takes inputs (i.e. the hyperlink structures of the entire www) and cranks out a score for every webpage that, in theory, represents its authority on the www.

As we learn from the behavior economics of humans, when we put a score on something, we created an incentive for some people to want to improve their score. This is human nature. People care about themselves, they care about any comparisons that concern them, whether it is their webpages, car, home, their work, or just themselves. Some would go so far as to cheat the algorithm just to get a better score. In fact, Google’s PageRank has created an entire industry around gaming their score, and it’s called SEO. Although any SEO specialist may deny the fact that they are gaming the PageRank algorithm, they are constantly finding ways to artificially increase the PageRank of your webpages. Is this cheating? Some SEO schemes may be acceptable by Google, but there are definitely some that are considered cheating (e.g link farm and spamdexing).

The New Story of Influence Engine Optimization

Today, the social web has grown and gained massive adoption. And again, influence vendors are putting a number on something (i.e. people’s influence). Moreover, they made this score very public and visible. So people will again find ways to artificially increase their influence score. But there are three aspects that are different this time.

Influence scoring algorithms are much more susceptible to gaming than PageRank, because someone’s influence score depends heavily on his own behavior (see Klout is Broken). This should be apparent from the fact that all influence scores are computed from people’s social media activities data.

Unlike the PageRank score of a page, someone’s influence score is feedback directly to himself. This means we won’t need an IEO expert to tell someone how to behave in order to increase his influence score. A user can easily discover the effects of his own behaviors on his influence score all by himself. So not only is the influence scoring algorithm more susceptible to gaming, they are also easier to game.

Finally, compared to Google, influence vendors have little to no mechanisms to discover and combat these cheating behaviors.

IEO is an inevitable consequence of scoring people’s influence. So, do influence scores still have any meaning? It’s definitely not a measure of someone’s influence; and it’s probably not even a measure of his potential influence anymore due to IEO.

So an influence score is really just a measure of how well people game the influence scoring algorithm.

If you responded a lot to your twitter stream yesterday and your influence score jump up today, you’ve just discovered that you can increase your influence score by responding more. Knowing this, would you continue to respond more? Most people probably would, especially if they care about their score. This has created a lot of noisy chatters who are not actually influential in any meaningful way. Their influence score is merely a reflection of the fact that they have successfully gamed the algorithm into giving them a higher score simply by responding more, but not actually doing anything truly influential.

Here is the irony: Because behaviors that game the system are typically a lot easier and simpler (see Simplicity Counts - Even in Gamification) than behaviors that are truly influential, IEO will tend to changes people’s behavior in a way that pushes them further away from being truly influential. Ironic isn’t it? That’s why I call this “influence irony.”

Conclusion

SEO is a natural consequence of the fact that search engines (e.g. Google) is attaching a score (i.e. PageRank) to the webpages on the www. Today history is repeating itself as influence vendors are assigning an influence score to social media participants. Consequently, people will inadvertently change their behavior to game the influence scoring algorithms in order to optimize their score. Although influence engine optimization (IEO) is inevitable, and in many ways similar to SEO, three aspects of IEO differ from SEO:

Influence scoring algorithms are more susceptible to gaming than PageRank

These algorithms can be gamed easily by the participants themselves (i.e. no IEO experts required)

Influence vendors have little defense against these cheating behaviors

What does this means? It means influence scores will become less and less accurate as a measure of someone’s potential influence, and more of a reflection of how successful someone has gamed the influence scoring algorithm.

It is quite a pity that the influence vendors not only failed to quantify people’s potential influence, but also created lots of loud and noisy wannabes who really haven’t done anything influential. But don’t be disappointed. Next time, let’s talk about how we can fix this. And there is a solution out there! If you’ve gotten this far with my writing on influence, don’t miss the next post! So stay tuned or follow me on twitter/Google+.

By the way, a good synopsis of some of my recent writing on influenced has been published on TechCrunch as well as WIRED UK. So if you like to review the main points of my previous posts without getting all the gory detail, you should check them out. Meanwhile, let’s open the floor to further discussions.

Nice article Michael. I noticed in your tag line that CRM magazine voted you a 2010 'Infuential' Leader I share your cynicism for current social media based influence measures. I think in the end of the day when you nominate someone as influential you should be putting your own reputation at risk. Its like the Net Promoter Score. If I recommend someone and it doesn't turn out to work out then it relfects badly on me. Of course it works the other way around as well, but I think that if their was a penalty for a bad recommendation then we would get less noise and a more accurate idication of influence.

Thank you for the comment, and I'm glad to hear that you share the same persepctive. However, I wouldn't say it's just out of cynicism. I really want the influence vendors to do the right thing and properly identify people who have real influences rather than just the loud and noisy people. As I said, next time we will talk about how to fix the influence irony (i.e. influence engine optimization).

I think that your idea of penalizing bad recommendations would work in theory. However, many studies in persuasive technologies have demonstrated that penalties often cause people to abandon the behavior completely. That is people siimply don't recommend anything anymore. Therefore, even though algorithmically, penalizing bad recommendation should work, in practice, it fails because of the users would no longer have the motivation to recommend anymore. The failure will arise because of the human element, not because of the strategy or alogirhtm.

Instead of a penalty for the recommender, what about a much smaller adjustment to their influencer score - a larger adjustment (weight) for a great recommendation? It wouldn't eliminate the noise but make the signal more obvious. I guess this depends on how accurately you detect true influence. At any rate, I'm looking forward to the big reveal in the next post!

I look forward to reading about your potential solution. In the simplest terms, the best thing we can all do as consumers of input is to not give credibility to the scores. The more we weight them, the more they will be perceived as relavent.

I think scores like klout are a bit bit analogous to the likes of NPS. Even if you assume that klout is valid in the sense that it can't be gamed (which we know is not true), it still really doesn't even measure true influence. Because someone is retweeted or quoted or "liked", does that translate into behavior on the part of the retweeter, quoter or liker? As NPS asks "would you recommend?" I'm much more willing to answer "yes" to that because it costs me nothing to do so. But, if I'm asked "did you recommend?" "did those you recommended to do anything?" That requires work. And I'm less likely to answer yes.

When I "like" a brand. Does that mean I made a purchase? Does that mean I'm a advocate? Heck no. But if the price for everyone who "likes" something was that they had to take action to vallidate their like to prove that the person/brand being liked is actually driving behavior, then the number of those likes plummets, IMHO.

Thank you for joining the discussion. I apologize for the late reply as I've been out at Deloitte University for ON Social Insight 2.

Experimentally, the negative adjustment to user's influence score will be perceived as a penalty by the user. And that is often more than enough to cause users to abandon the platform. Moreover, tuning the adjustment amount is very tricky. If it is too small, then people don't get it or don't care, then they will continue to recommend junks. If it is too large, then people abandon the behavior.

That is why there is no negativity in our rank and reputation system that is end user facing. We simply use the absense of positive reward and social pressure to drive the positive behavior of providing good content.

Thank you for the comment and joining the discussion. I've been on the road and thus for the late reply.

You are definitely right about the transitivity of action. This is also another problem with influence measurement companies. Basically they way over-generalized by assuming that if someone can influence you to retweet, share, like, re-pin, etc. then they can influence you to do other thing, such as purchase, referral, recommendation, etc. That is almost never the case.

Influencing people to retweet, share or like something is way simpler an action than influencing people to puchase, refer or recommend something. One does not imply the other, because as you pointed out, the effort required to perform either is very different.

Simplicity can sometimes drive action just because the behavior require so little resources that people's cognitive surplus is sufficient to provide the resources needed to drive that behavior. It doesn't mean they are advocate or even really like something when people click the like button.They could just be clicking around randomly and accidentally clicked like and didn't bother to unclick it. There could be a million reason.

So definitely... if you require any extra work, such as validation, or even if it is simply entering a password when liking something, the number of likes will drop.

Mike, great series of articles on the Influence field. I've discovered your posts in the Text Analysis group on LinkedIn, which led me to your website.

As a long-dated SEO professional, I'd like to pinpoint an anachronism in the first sentence of your conclusion, that is, "SEO is a natural consequence of the fact that Google is attaching a score (i.e. PageRank) to the webpages on the www", which perpetuates the common but wrong belief that SEO came

1) after PageRank, and

2) thanks to Google.

SEO PRIOR PAGERANK

A little bit of History.

I first heard about the term SEO in 1997, to name a practice that was born way before at pretty much the same time of the first major search engines in the early 90's.

The PageRank patent was initially filed in January 1998 by the Standford University with Larry Page (while still student at Standford) as its inventor; and was granted in 2001.

SEO EVEN PRIOR GOOGLE

I remember joining in 1992 a private BBS-then-NNTP community that started "gaming" search engines, practice which was called SEO no later than 1997, hence, before Google Inc. was incorporated in September 1998 and released officially its search engine to the public.

PROPOSED FIX

So one should read instead, for instance, "SEO is a natural consequence of the fact that search engines are computing a score, called page rank (do not confuse with Google's PageRank), to rank web pages in search engine results pages (SERP)."

Getting a step back, it's interesting to notice how a detail that is somehow insignificant compared to the whole piece of content can distract a sharp-eyed reader and hamper his mind from fully and peacefully enjoying the true value of an article.

Now that you pointed out, it makes total sense. SEO probably exist as soon as search engines are computing relevance scores that rank orders the search results in the SERP. I guess Google, came up as a dominant player in the web search engine industry, made SEO more popular. But I don't think this is specific to Google though. If Yahoo, Bing, or whoever, were the dominant web search engine, whatever ranking criteria they use will probably determine a lot of the SEO practice in the industry. Because that is where people want their page to rank higher.

Sorry i'm really late on this one... Extremely interersting article. Just few words about Klout. Usually, i like to be gamified and i always find interesting to trick a system. And Klout is great for that, specially when you don't have too much time to spend since it's really easy.

Last fun trick/recipe that kinda surprised me :

- A brand/group/page is publishing a new event on Facebook (A concert or band tour announcement is a perfect example)

- They publish a new picture as timeline cover

- You tag yourself in the picture

- Wait few days (important ingredient of the recipe)

- Enjoy your new ironic Klout Score !!

During weeks if not months, while others will do the same, Klout will keep telling you this nice (for your score) words :

- You were tagged in a photo (obviously true since you did it yourself !)

Thank you for the comment. I also apologize for the late reply. I'm totally swamped too. But it's never too late to comment on any of my blogs. I will always respond, although it may not be immediately.

Well, you have just provided an excellent example of IEO (how to raise your influence score artificially through tactical methods, rather than doing something truly influential). So basically you’ve found that tagging your photos at a concert photo posted on FB timeline will increase your influence score, so if you want to increase your influence score (which is not a good reflection of your true influence), you can just go on tagging yourself in concert photos.

This is a great example that I didn’t know about. I will definitely mention it in my future speaking engagements when I’m talking about this subject.

It is true that IEO can be quite fun (so does SEO). That’s why people WILL game the influence scoring system. It’s also what I meant when I said that influence scoring system are “more susceptible to gaming.” In fact, when people can do something that can directly affect the outcome, and they can see that outcome, this kind of bio-feedback is very motivating. It is one of the intrinsic motivators. Dan Pink call it Mastery; other motivational psychologist call it Competence. They are really the same.

However, this is also why brands should NOT trust the influence score provided by influence vendors today. If influence vendors don’t take measure against IEO, their influence score it actually not measuring influence at all. So how can we fight IEO? That is the subject of the next post, which is already posted!

Thanks for your feedback and in-depth explanations. Well, if you really plan to talk about this example sometimes, here's an image to make it all clearer i think:

On January 19 i've tagged myself on this picture (Facebook timeline page cover), announcing a World Tour.

I have to say here that it wasn't to trick Klout on purpose : i'm a real fan here !

Since then, everytime somebody else is doing the same, it brings the image up again in My Klout Moments and probably brings me many points since there's +3K persons tagged.

What is interesting in this screenshot is about the wording. "Your were tagged in a photo" : WRONG, it's the opposite, i've tagged myself. "You engaged 3K others" : WRONG it's not about engagment at all. It's just a 'click&run' action.

The whole thing is going on since 1,5 month and i find it very convenient !!

Thank you for providing a detail documentation of the example with an picture that explains better than my thousand words.

It's definitely a very cool trick. And believe me, no matter what klout says about your influence, you have definitely influenced me by having timely, relevant content that is useful to me. As I said before in an earlier post: relevance and timing actually is a very important factor that drives influence. Because it determine my capacity to be influenced.

Interesting thanks Michael - would like to know what your solution is. I know from my limited research into influencer marketing platforms that possibly a tool like awedience which focuses on keywords in people's Twitter profile and tweets can reduce the gaming, especially when the results are crossed checked vs all 3 main influencer tools, Kred, Klout and PeerIndex. Or is it about tying in influencer data with specific CRM data round customers to ensure a more lasting and measurable financial business benefit?

I was actually responding ot your comment on twitter about your klout dropping below 50, and was trying to tell you that klout probably doesn't matter in terms of your true influence as my comment to Arnaud above.

However, I do like to clarify one important point: measuring influence is different from measuring the business impact of influence.

The Adaptive Algorithm that I talk about in the next post address the problem of quantifying influence. This is a necessary step before we can quantify the effect of infuence on business. Because if you can't even measure influence, you won't be able to accurately measure it's affect on your business. But having an accurate measure of influence is also not sufficient to know its business impact.

That is where the CRM data (i.e. the transaction data of the influencers) comes in. After you have an accurate measure of influence, then you can tie it back to CRM / financial / transactional data to see its affect on the business.